"Anomaly Detection: Recent Advances, AI and ML Perspectives and Applications" ed. by Venkata Krishna Parimala, et al.

This book discusses and addresses anomaly detection in the context of artificial intelligence and machine learning advancements. The book demystifies the challenges and presents solutions for detecting and understanding network anomalies. Building on the existing literature, this thorough and timely work is an invaluable resource.

It highlights various problems, offers workable solutions to those problems, and allows academic and professional researchers and practitioners to engage in new technologies linked to anomaly detection. Whether you are a seasoned network professional or an enthusiast keen on cyber security, this volume promises insights that will fortify our connected futures.

Contents
1. Anomaly Detection Recent Advances, AI and ML Perspectives and Applications
2. Anomaly Detection in Medical Time Series with Generative Adversarial Networks: A Selective Review
3. Anomaly Detection in IoT: Recent Advances, AI and ML Perspectives and Applications
4. Anomaly Detection in Time Series: Current Focus and Future Challenges
5. Anomaly Detection through Adaptive DASO Optimization Techniques
6. Anomaly Detection in Intrusion Detection Systems
7. Verification of Generalizability in Software Log Anomaly Detection Models


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